{"title":"Usability error classification: qualitative data analysis for UX practitioners","authors":"L. Gorlenko, Paul Englefied","doi":"10.1145/1125451.1125610","DOIUrl":null,"url":null,"abstract":"Usability evaluations generate large amounts of poorly structured qualitative data, but traditional methods of analysis are often impractical for use by industry practitioners. To address this, we developed a classification of usability issues covering cause, effect, task impact and business impact. In a design project, this has several applications, such as a) enabling practitioners to analyze qualitative data quickly and reliably; b) ensuring that findings can be systematically compared across studies; c) presenting results to clients in terms of potential business impact and its causes; and d) offering recommendations to designers in terms of design errors and their cost. We continue refining the model as we test it in our projects.","PeriodicalId":201154,"journal":{"name":"CHI '06 Extended Abstracts on Human Factors in Computing Systems","volume":"67 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"CHI '06 Extended Abstracts on Human Factors in Computing Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1125451.1125610","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9
Abstract
Usability evaluations generate large amounts of poorly structured qualitative data, but traditional methods of analysis are often impractical for use by industry practitioners. To address this, we developed a classification of usability issues covering cause, effect, task impact and business impact. In a design project, this has several applications, such as a) enabling practitioners to analyze qualitative data quickly and reliably; b) ensuring that findings can be systematically compared across studies; c) presenting results to clients in terms of potential business impact and its causes; and d) offering recommendations to designers in terms of design errors and their cost. We continue refining the model as we test it in our projects.